Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model
| Title: | Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model |
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| Language: | English |
| Authors: | Xin Tang, Zhiqiang Yuan (ORCID |
| Source: | Journal of Computer Assisted Learning. 2025 41(1). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Influences, College Students, Student Behavior, Intention, Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Barriers, Foreign Countries, Performance, Expectation, Social Influences, Educational Environment, Value Judgment, Student Attitudes, Habit Formation |
| Geographic Terms: | China |
| DOI: | 10.1111/jcal.13105 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | Background: Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors of society and is anticipated to revolutionise the educational landscape, especially in the domain of tertiary education. However, there is a gap in understanding factors influencing university students' behavioural intention to use generative AI, leading to hesitation in its adoption. Objectives: The primary objective of this study was to investigate the factors that influence university students' behavioural intention to engage with and utilise generative AI. The study sought to delve into the fundamental reasons and obstacles that university students encounter when contemplating the adoption of this technology for their academic endeavours. Methods: The study used a quantitative research design, utilising a revised version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data were collected from a sample of 380 university students in Changsha, the capital city of Hunan in China. Partial least squares structural equation modelling (PLS-SEM) was used to analyse the relationships between the variables of the model, which included performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), learning value, habit and behavioural intention. Results: The analysis revealed that PE and EE have a direct impact on learning value. Additionally, SI and FC were found to directly affect the formation of habit. Among these factors, learning value emerged as the most potent predictor of university students' behavioural intention to use generative AI. Habit also demonstrated a significant, albeit smaller, effect on behavioural intention. Conclusions: The study's findings underscore the importance of learning value in driving the adoption of generative AI among university students. Efforts to enhance the learning value of generative AI could significantly increase its uptake in higher education. Furthermore, the role of habit, while less pronounced, suggests that consistent exposure and use can foster a greater inclination towards generative AI. These insights provide a foundation for targeted interventions aimed at improving the integration and application of generative AI within educational settings. Stakeholders, including educators, policymakers and designers of generative AI, can leverage these findings to create an environment conducive to the adoption and effective use of generative AI in higher education. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1459123 |
| Database: | ERIC |
| ISSN: | 0266-4909 1365-2729 |
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| DOI: | 10.1111/jcal.13105 |
| Published in: | Journal of Computer Assisted Learning |
| Language: | English |